Nitrogen fertilizer is one of the main costs associated with growing food crops, including grain crops such as corn, wheat, and rice. To a point, nitrogen increases crop yield at a very cost-efficient rate; beyond that point, some amount of over-application does not harm the plant. Farmers therefore often err on the side of caution by over-applying nitrogen fertilizer to their crops. World-wide annual consumption of nitrogen fertilizer is 500 million short tons, up to an estimated 50% of which is not needed to achieve optimum yield.
There are downsides to the over-application of nitrogen fertilizer, including the needless expense to the grower and the risks posed to water and other aspects of the ecosystem affected by runoff. It would therefore be advantageous to accurately identify an optimal amount of nitrogen fertilizer to be applied to maximize yield of the crop without using more than needed.
Nitrogen content and chlorophyll content in a plant are interrelated, so by estimating the amount of chlorophyll in a plant, the amount of nitrogen present (and therefore the amount of nitrogen to be applied) can be determined. Known techniques for estimating chlorophyll from images of the plant rely on the color of the leaf. Yet “color” describes the response of the eyes and brain to the visible spectrum, and is therefore inapplicable and unreliable in computer-based chlorophyll measurement. Estimating chlorophyll content also requires information about the structure of the leaf, including its index of refraction, which varies from species to species. Known methods typically used a “published” value of a leaf model parameter, N. Yet this approximation of N for a given leaf does not take into account the actual variations in N from leaf to leaf.